OPENJ: A Conceptual Framework for Open-Source Digital Human Modeling and Ergonomic Assessment in a CAD Environment

TL;DR

This paper introduces OPENJ, a conceptual framework aiming to develop an open-source digital human modeling and ergonomic assessment tool integrated with CAD, addressing accessibility and cost barriers of commercial systems.

Contribution

It provides a detailed design blueprint for an open-source DHM platform, promoting wider adoption and collaborative development in ergonomic assessment.

Findings

Identifies barriers of commercial DHM tools for small organizations.

Proposes a comprehensive open-source framework for ergonomic assessment.

Lays groundwork for community-driven DHM development.

Abstract

Industrial workplace challenges range from musculoskeletal disorders -- a leading cause of occupational injury -- to suboptimal workstation layouts, inefficient task sequences, and poor human-equipment fit. Digital human modeling (DHM) tools address several of these challenges by placing a scalable virtual mannequin in a computer-aided design (CAD) environment, enabling engineers to evaluate ergonomic risk through standardized assessment methods (RULA, REBA, NIOSH Lifting Equation, OWAS), optimize workstation layouts for reach and visibility, predict task postures through inverse kinematics, and simulate operations before physical implementation. Despite four decades of development since the Jack system originated at the University of Pennsylvania in the 1980s, the integrated DHM capability set -- anthropometric mannequin, posture prediction, ergonomic assessment, and CAD integration -- remains exclusive to commercial platforms such as Siemens Tecnomatix Jack (Process Simulate), Dassault DELMIA, Humanetics RAMSIS, and the University of Iowa's Santos system. These platforms operate under proprietary, vendor-quoted pricing models, and their acquisition and operating costs, together with closed-source implementations, have been repeatedly identified as practical adoption barriers for individual researchers, small-to-medium enterprises, and educational institutions. Organizations without access resort to manual observational methods -- paper-based worksheets applied to photographs or video -- sacrificing the predictive power and reproducibility that computational analysis provides. The paper serves as a design blueprint for (OpenJane/Joe), positioning the project for subsequent open-source implementation and community adoption.